A Spectrum-Efficient Solution with Data Rate Guarantees in 5G/Next-G Networks

The scarcity of spectrum and the proliferation of data-intensive applications in 5G/Next-G networks call for innovations of new techniques that are capable of offering UE-level data rate guarantee with minimum required spectrum usage. This is a challenging problem due to the complexity of mechanisms involved in the process, such as Resource Block (RB) allocation, modulation and coding scheme (MCS) selection, and MU-MIMO beamforming (BF) design. Further complicating the problem is the random, unknown nature of Channel State Information (CSI) and the errors involved in its estimation. In this paper, we present Rudra, which offers a comprehensive solution to these challenges. Rudra formulates the bandwidth minimization problem by incorporating probabilistic data rate guarantee through a chance constraint, which embeds RB allocation, MCS selection, and MU-MIMO BF mechanisms. The CSI uncertainty problem is addressed through a novel error-embedded (EE)-Wasserstein ambiguity set based on a small set of data samples. We show that the solution by Rudra meets our design objective and outperforms a modified state-of-the-art algorithm.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-01-01
DOI
https://doi.org/10.1109/jiot.2025.3595817
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

A Spectrum-Efficient Solution with Data Rate Guarantees in 5G/Next-G Networks

Wenjing Lou, Shaoran Li, Yubo Wu, Y. Thomas Hou et al.
IEEE Internet of Things Journal
Advanced MIMO Systems Optimization
article

A Spectrum-Efficient Solution with Data Rate Guarantees in 5G/Next-G Networks

Wenjing Lou, Shaoran Li, Yubo Wu, Y. Thomas Hou, Nan Jiang, Shiva Acharya
article en

Abstract

The scarcity of spectrum and the proliferation of data-intensive applications in 5G/Next-G networks call for innovations of new techniques that are capable of offering UE-level data rate guarantee with minimum required spectrum usage. This is a challenging problem due to the complexity of mechanisms involved in the process, such as Resource Block (RB) allocation, modulation and coding scheme (MCS) selection, and MU-MIMO beamforming (BF) design. Further complicating the problem is the random, unknown nature of Channel State Information (CSI) and the errors involved in its estimation. In this paper, we present Rudra, which offers a comprehensive solution to these challenges. Rudra formulates the bandwidth minimization problem by incorporating probabilistic data rate guarantee through a chance constraint, which embeds RB allocation, MCS selection, and MU-MIMO BF mechanisms. The CSI uncertainty problem is addressed through a novel error-embedded (EE)-Wasserstein ambiguity set based on a small set of data samples. We show that the solution by Rudra meets our design objective and outperforms a modified state-of-the-art algorithm.

IEEE Internet of Things Journal
Georgia Institute of Technology (US), Nvidia (United States) (US), Virginia Tech (US)
National Science Foundation, Office of Naval Research
Openalex Percentile: Top 82%
Advanced MIMO Systems Optimization
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